20 August 2021 Discrimination of multi-crop scenarios with polarimetric SAR data using Wishart mixture model
Nilam Chaudhari, Suman K. Mitra, Sanid Chirakkal, Srimanta Mandal, Deepak Putrevu, Arundhati Misra
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Abstract

Discrimination of crop varieties spanned over heterogeneous agriculture land is a vital application of polarimetric SAR images for agriculture monitoring and assessment. The covariance matrix of polarimetric SAR images is observed to follow a complex Wishart distribution for major classification tasks. It is true for homogeneous regions, but for heterogeneous regions, the covariance matrix follows a mixture of multiple Wishart distributions. We aim to improve the classification accuracy when the terrain under observation is heterogeneous. For this purpose, Wishart mixture model is employed along with expectation-maximization (EM) algorithm for parameter estimation. Elbow method helps us to devise the number of mixtures. The convergence of the EM algorithm depends on the choice of initial points. So, to improve the robustness of the model, different initialization approaches, such as random, K-means, and global K-means, are embedded in the EM algorithm. Further, the degrees of freedom is one of the crucial parameters of Wishart distribution. Therefore, the impact of different degrees of freedom is analyzed on classification accuracy. The method that is equipped with initialization technique along with optimum degrees of freedom is assessed using three full polarimetric SAR data sets of agriculture lands. The first two are benchmark data sets of Flevoland, Netherlands, region acquired by AIRSAR sensor, and third is our study area of Mysore, India, acquired by RADARSAT-2 sensor.

© 2021 Society of Photo-Optical Instrumentation Engineers (SPIE) 1931-3195/2021/$28.00 © 2021 SPIE
Nilam Chaudhari, Suman K. Mitra, Sanid Chirakkal, Srimanta Mandal, Deepak Putrevu, and Arundhati Misra "Discrimination of multi-crop scenarios with polarimetric SAR data using Wishart mixture model," Journal of Applied Remote Sensing 15(3), 034514 (20 August 2021). https://doi.org/10.1117/1.JRS.15.034514
Received: 10 February 2021; Accepted: 4 August 2021; Published: 20 August 2021
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KEYWORDS
Expectation maximization algorithms

Synthetic aperture radar

Polarimetry

Data modeling

Speckle

Agriculture

Scattering

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